Data Acceleration: Architecture for the Modern Data Supply Chain

Data acceleration helps organizations address three key challenges—movement, processing and interactivity—and more rapidly discover insights. Unlock the value hidden from your data. By treating data as a supply chain, you can enable it to flow easily and usefully throughout the entire organization.

Overview

Accenture’s Big Data team and Accenture Technology Labs examined three data-related challenges: movement, processing and interactivity. More importantly, we’ve pragmatically explored the full landscape of architectural components to address these challenges.

We have learned that organizations can leverage many different data technology components and combinations to build a modern data supply chain that provides data acceleration. With data acceleration, companies can move data swiftly from its source to where it is needed, process data to gain actionable insights quickly, and foster faster data connections and query responses from users or applications.

Read more about the strengths, weaknesses and compatibility of components that include big data platforms, in-memory databases, cache clusters and more.

Data technologies are evolving rapidly, but organizations have adopted most of these in a piecemeal fashion. As a result, enterprise data is vastly underutilized. It is time for a change, especially as new external data sources are becoming available, providing fresh opportunities for data insights.

In addition, the tools and technology required to build a better data platform are available and in use. These provide a foundation on which companies can construct an end-to-end data supply chain.

Data acceleration plays a major role in a robust data supply chain. With data acceleration, organizations gain quick access to valuable data—which enables them to perform analysis on the data, gain insights and take action in the sometimes very small window of opportunity available to businesses.

Analysis

Data acceleration can help organizations address three challenges:

MovementTo extract valuable insights from data in this new world, organizations need to harness it from multiple sources without losing any of it, and deliver it for processing and storage. Moving the data from its origin to where it is needed in the organization can seem like drinking from a fire hose while trying not to lose a single drop. Data acceleration enables multiple ways of successfully bringing data into an organization’s data infrastructure and ensuring that it can be referenced quickly.

ProcessingThe volumes and variety of data requiring processing have ballooned. Companies need to perform calculations on the data, create and execute simulation models, and compare data statistics faster than ever. Good analytical technology can pre-process incoming data, but better technology combines streaming data with historical (modeled) data to enable more intelligent decision making. Data acceleration supports faster processing by leveraging advances in hardware and software for computer clusters, enabling them to operate more efficiently than ever.

InteractivityInteractivity is about usability of the data infrastructure. Traditional solutions have made it easy for people to submit queries to get the results they need to arrive at actionable insights. However, the rise of big data has led to new programming languages that discourage existing users from adopting the systems. Data acceleration supports faster interactivity by enabling users and applications to connect to the data infrastructure in universally acceptable ways and by ensuring that query results are delivered as quickly as required.

Recommendations

A data supply chain can accelerate data movement, processing and interactivity—enabling decision makers to more swiftly capture and act on insights from their data.

To begin building a data supply chain strategy that supports data acceleration in your organization:

Inventory your data. Start with your most frequently accessed and time-relevant data. This will be given first access to your data platform and accelerated on the platform.

Identify inefficient processes. Look for any manual, time-consuming data curation processes, such as tagging or cleansing. These may be candidates for replacement with machine learning algorithms.

Identify data silos. Along with silos, identify corresponding data needs that are currently unmet across the business.

Simplify data access. Create a strategy for standardizing data access via the data platform. Solutions may be hybrid, combining traditional middleware and API management, or even a platform-as-a-service offering.

Consider external data sources. Look outside your organization for external data sources that can be incorporated to complement existing data and help create more complete insights.

An organization must be able to generate business insights from enterprise data in order to gain a competitive advantage. Building a data supply chain that supports data acceleration will put your business on the path to data-driven outcomes.